SoroushVahidi/scidocs huggingface dataset:
Does Language Shape Thought?: Mandarin and English Speakers' Conceptions of Time
(2001)

Does the language you speak affect how you think about the world? This question is taken up in three experiments. English and Mandarin talk about time differently--English predominantly talks about time as if it were horizontal, while Mandarin also commonly describes time as vertical. This difference between the two languages is reflected in the way their speakers think about time. In one study, Mandarin speakers tended to think about time vertically even when they were thinking for English (Mandarin speakers were faster to confirm that March comes earlier than April if they had just seen a vertical array of objects than if they had just seen a horizontal array, and the reverse was true for English speakers). Another study showed that the extent to which Mandarin-English bilinguals think about time vertically is related to how old they were when they first began to learn English. In another experiment native English speakers were taught to talk about time using vertical spatial terms in a way similar to Mandarin. On a subsequent test, this group of English speakers showed the same bias to think about time vertically as was observed with Mandarin speakers. It is concluded that (1) language is a powerful tool in shaping thought about abstract domains and (2) one's native language plays an important role in shaping habitual thought (e.g., how one tends to think about time) but does not entirely determine one's thinking in the strong Whorfian sense.
As time goes by : Evidence for two systems in processing space 4 ime metaphors
(2002)

Temporal language is often couched in spatial metaphors. English has been claimed to have two space-time metaphoric systems: the ego-moving metaphor, wherein the observer’s context progresses along the time-line towards the future, and the time-moving metaphor, wherein time is conceived of as a river or conveyor belt on which events are moving from the future to the past. In three experiments, we investigated the psychological status of these metaphors by asking subjects to carry out temporal inferences stated in terms of spatial metaphors. In Experiment 1, we found that subjects were slowed in their processing when the assertions shifted from one spatial metaphoric system to the other. In Experiment 2, we determined that this cost of shifting could not be attributed to local lexical factors. In Experiment 3, we again found this metaphor consistency effect in a naturalistic version of the study in which we asked commonsense time questions of passengers at an airport. The results of the three studies provide converging evidence that people use spatial metaphors in temporal reasoning. Implications for the status of metaphoric systems arc discussed.
Metaphoric structuring: understanding time through spatial metaphors
(2000)

The present paper evaluates the claim that abstract conceptual domains are structured through metaphorical mappings from domains grounded directly in experience. In particular, the paper asks whether the abstract domain of time gets its relational structure from the more concrete domain of space. Relational similarities between space and time are outlined along with several explanations of how these similarities may have arisen. Three experiments designed to distinguish between these explanations are described. The results indicate that (1) the domains of space and time do share conceptual structure, (2) spatial relational information is just as useful for thinking about time as temporal information, and (3) with frequent use, mappings between space and time come to be stored in the domain of time and so thinking about time does not necessarily require access to spatial schemas. These findings provide some of the first empirical evidence for Metaphoric Structuring. It appears that abstract domains such as time are indeed shaped by metaphorical mappings from more concrete and experiential domains such as space.
Bayesian Gaussian process models : PAC-Bayesian generalisation error bounds and sparse approximations
(2003)

Non-parametric models and techniques enjoy a growing popularity in the field of machine learning, and among these Bayesian inference for Gaussian process (GP) models has recently received significant attention. We feel that GP priors should be part of the standard toolbox for constructing models relevant to machine learning in the same way as parametric linear models are, and the results in this thesis help to remove some obstacles on the way towards this goal. In the first main chapter, we provide a distribution-free finite sample bound on the difference between generalisation and empirical (training) error for GP classification methods. While the general theorem (the PAC-Bayesian bound) is not new, we give a much simplified and somewhat generalised derivation and point out the underlying core technique (convex duality) explicitly. Furthermore, the application to GP models is novel (to our knowledge). A central feature of this bound is that its quality depends crucially on task knowledge being encoded faithfully in the model and prior distributions, so there is a mutual benefit between a sharp theoretical guarantee and empirically well-established statistical practices. Extensive simulations on real-world classification tasks indicate an impressive tightness of the bound, in spite of the fact that many previous bounds for related kernel machines fail to give non-trivial guarantees in this practically relevant regime. In the second main chapter, sparse approximations are developed to address the problem of the unfavourable scaling of most GP techniques with large training sets. Due to its high importance in practice, this problem has received a lot of attention recently. We demonstrate the tractability and usefulness of simple greedy forward selection with information-theoretic criteria previously used in active learning (or sequential design) and develop generic schemes for automatic model selection with many (hyper)parameters. We suggest two new generic schemes and evaluate some of their variants on large real-world classification and regression tasks. These schemes and their underlying principles (which are clearly stated and analysed) can be applied to obtain sparse approximations for a wide regime of GP models far beyond the special cases we studied here.
Romantic Relationships among Young Adults : An Attachment Perspective
(2012)

The present study used adult attachment as a theoretical framework to investigate individual differences in young adults’ romantic relationships. Emphasis was put on studying the relational aspects of the self and coping strategies in an attempt to better understand how romantic partners feel about themselves and how they subsequently behave in periods of conflict. The sample comprised undergraduate university students (N=377) who responded to a self report questionnaire in English. Most findings were consistent with the proposed hypotheses. Results indicated that anxious individuals tend to report an ambivalent coping style and that fearful individuals were most vulnerable to health risks such as depression. Gender differences were also explored and results showed for instance that women generally tend to seek more support, and men might be more dismissing than women.
Block-diagonal Hessian-free Optimization for Training Neural Networks
(2017)

Second-order methods for neural network optimization have several advantages over methods based on first-order gradient descent, including better scaling to large mini-batch sizes and fewer updates needed for convergence. But they are rarely applied to deep learning in practice because of high computational cost and the need for model-dependent algorithmic variations. We introduce a variant of the Hessian-free method that leverages a block-diagonal approximation of the generalized Gauss-Newton matrix. Our method computes the curvature approximation matrix only for pairs of parameters from the same layer or block of the neural network and performs conjugate gradient updates independently for each block. Experiments on deep autoencoders, deep convolutional networks, and multilayer LSTMs demonstrate better convergence and generalization compared to the original Hessian-free approach and the Adam method.
A 0.018% THD+N, 88-dB PSRR PWM Class-D Amplifier for Direct Battery Hookup
(2012)

A low-distortion third-order class-D amplifier that is fully integrated into a 0.18-μ m CMOS process was designed for direct battery hookup in a mobile application. A class-D amplifier for direct battery hookup must have a sufficiently high power supply rejection ratio (PSRR) in preparation for noise, such as when a global system for mobile communications (GSM) bursts ripples through the system power line. This amplifier has a high PSRR of 88 dB for 217-Hz power supply ripples, using a third-order loop filter. System performance and stability are improved by applying the design technique of input-feedforward delta-sigma (ΔΣ) modulators to the pulse-width modulation (PWM) class-D amplifier. A filterless method that can remove the external LC filter is employed, which offers great advantages in terms of PCB space and system cost. This amplifier achieves a power efficiency of 85.5% while delivering an output power of 750 mW into an 8-Ω load from a 3.7-V supply voltage. Maximum achieved output power at 1% total harmonic distortion plus noise (THD+N) from a 4.9-V supply voltage into an 8-Ω load is 1.15 W. This class-D amplifier is designed to have a broad operational range of 2.7-4.9 V for the direct use of mobile phone battery power. It has a total area of 1.01 mm2 and achieves a THD+N of 0.018%.
SeiSIM : Structural Similarity Evaluation for Seismic Data Retrieval
(2014)

Structural similarity evaluation is a critical step for retrieving existing databases to find matching records for a given seismic data set. The objective is to enable the re-use of historical findings to assist exploration with new survey data. Currently there are very few structural similarity metrics specifically designed for seismic data, especially seismic survey maps. In this paper, we propose a metric that combines texture similarity and geological similarity, which is derived from discontinuity maps. We test the seismic similarity metric in a retrieval application. The successful results indicate that our approach is promising.
A BIOLOGICALLY INSPIRED VISUAL WORKING MEMORY
(2018)

The ability to look multiple times through a series of pose-adjusted glimpses is fundamental to human vision. This critical faculty allows us to understand highly complex visual scenes. Short term memory plays an integral role in aggregating the information obtained from these glimpses and informing our interpretation of the scene. Computational models have attempted to address glimpsing and visual attention but have failed to incorporate the notion of memory. We introduce a novel, biologically inspired visual working memory architecture that we term the Hebb-Rosenblatt memory. We subsequently introduce a fully differentiable Short Term Attentive Working Memory model (STAWM) which uses transformational attention to learn a memory over each image it sees. The state of our HebbRosenblatt memory is embedded in STAWM as the weights space of a layer. By projecting different queries through this layer we can obtain goal-oriented latent representations for tasks including classification and visual reconstruction. Our model obtains highly competitive classification performance on MNIST and CIFAR-10. As demonstrated through the CelebA dataset, to perform reconstruction the model learns to make a sequence of updates to a canvas which constitute a parts-based representation. Classification with the self supervised representation obtained from MNIST is shown to be in line with the state of the art models (none of which use a visual attention mechanism). Finally, we show that STAWM can be trained under the dual constraints of classification and reconstruction to provide an interpretable visual sketchpad which helps open the ‘black-box’ of deep learning.
FRIENDSHIPS IN ONLINE PEER-TO-PEER LENDING : PIPES , PRISMS , AND RELATIONAL HERDING 1
(2015)

This paper investigates how friendship relationships act as pipes, prisms, and herding signals in a large online, peer-to-peer (P2P) lending site. By analyzing decisions of lenders, we find that friends of the borrower, especially close offline friends, act as financial pipes by lending money to the borrower. On the other hand, the prism effect of friends’ endorsements via bidding on a loan negatively affects subsequent bids by third parties. However, when offline friends of a potential lender, especially close friends, place a bid, a relational herding effect occurs as potential lenders are likely to follow their offline friends with a bid.
Deep Neural Networks for Learning Graph Representations
(2016)

In this paper, we propose a novel model for learning graph representations, which generates a low-dimensional vector representation for each vertex by capturing the graph structural information. Different from other previous research efforts, we adopt a random surfing model to capture graph structural information directly, instead of using the samplingbased method for generating linear sequences proposed by Perozzi et al. (2014). The advantages of our approach will be illustrated from both theorical and empirical perspectives. We also give a new perspective for the matrix factorization method proposed by Levy and Goldberg (2014), in which the pointwise mutual information (PMI) matrix is considered as an analytical solution to the objective function of the skipgram model with negative sampling proposed by Mikolov et al. (2013). Unlike their approach which involves the use of the SVD for finding the low-dimensitonal projections from the PMI matrix, however, the stacked denoising autoencoder is introduced in our model to extract complex features and model non-linearities. To demonstrate the effectiveness of our model, we conduct experiments on clustering and visualization tasks, employing the learned vertex representations as features. Empirical results on datasets of varying sizes show that our model outperforms other stat-of-the-art models in such tasks.
Deep Learning for Video Classification and Captioning
(2016)

Accelerated by the tremendous increase in Internet bandwidth and storage space, video data has been generated, published and spread explosively, becoming an indispensable part of today's big data. In this paper, we focus on reviewing two lines of research aiming to stimulate the comprehension of videos with deep learning: video classification and video captioning. While video classification concentrates on automatically labeling video clips based on their semantic contents like human actions or complex events, video captioning attempts to generate a complete and natural sentence, enriching the single label as in video classification, to capture the most informative dynamics in videos. In addition, we also provide a review of popular benchmarks and competitions, which are critical for evaluating the technical progress of this vibrant field.
Generalized Sampling and Infinite-Dimensional Compressed Sensing
(2016)

We introduce and analyze a framework and corresponding method for compressed sensing in infinite dimensions. This extends the existing theory from finite-dimensional vector spaces to the case of separable Hilbert spaces. We explain why such a new theory is necessary by demonstrating that existing finitedimensional techniques are ill-suited for solving a number of key problems. This work stems from recent developments in generalized sampling theorems for classical (Nyquist rate) sampling that allows for reconstructions in arbitrary bases. A conclusion of this paper is that one can extend these ideas to allow for significant subsampling of sparse or compressible signals. Central to this work is the introduction of two novel concepts in sampling theory, the stable sampling rate and the balancing property, which specify how to appropriately discretize an infinite-dimensional problem.
Walking away from the Desktop Computer: Distributed Collaboration and Mobility in a Product Design Team
(1996)

A study of a spatially distributed product design team shows that most members are rarely at their individual desks. Mobility is essential for the use of shared resources and for communication. It facilitates informal interactions and awareness unavailable to colleagues at remote sites. Implications for technology design include portable and distributed computing resources, in particular, moving beyond individual workstation-centric CSCW applications.
Um estudo preliminar sobre Tipos de Personalidade em Equipes Scrum
(2012)

In software development, people have a fundamental role as the basis for a project’s success. Regarding agile methodologies, this factor is increased by the need of self-organized teams, which is related to its member’s personality and the relationships between them. This paper evaluates how the member’s personality types and social relations influence the outcome of Scrum teams, based on MBTI and sociometry. As a result it was possible to identify how psychological profiles influence the quality, productivity and achievement of goals defined in the Sprint Backlog.
Long short term memory for driver intent prediction
(2017)

Advanced Driver Assistance Systems have been shown to greatly improve road safety. However, existing systems are typically reactive with an inability to understand complex traffic scenarios. We present a method to predict driver intention as the vehicle enters an intersection using a Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). The model is learnt using the position, heading and velocity fused from GPS, IMU and odometry data collected by the ego-vehicle. In this paper we focus on determining the earliest possible moment in which we can classify the driver's intention at an intersection. We consider the outcome of this work an essential component for all levels of road vehicle automation.
Multi-Antenna Wireless Legitimate Surveillance Systems: Design and Performance Analysis
(2017)

To improve national security, government agencies have long been committed to enforcing powerful surveillance measures on suspicious individuals or communications. In this paper, we consider a wireless legitimate surveillance system, where a full-duplex multi-antenna legitimate monitor aims to eavesdrop on a dubious communication link between a suspicious pair via proactive jamming. Assuming that the legitimate monitor can successfully overhear the suspicious information only when its achievable data rate is no smaller than that of the suspicious receiver, the key objective is to maximize the eavesdropping non-outage probability by joint design of the jamming power, receive and transmit beamformers at the legitimate monitor. Depending on the number of receive/transmit antennas implemented, i.e., single-input single-output, single-input multiple-output, multiple-input single-output, and multiple-input multiple-output (MIMO), four different scenarios are investigated. For each scenario, the optimal jamming power is derived in a closed form and efficient algorithms are obtained for the optimal transmit/receive beamforming vectors. Moreover, low-complexity suboptimal beamforming schemes are proposed for the MIMO case. Our analytical findings demonstrate that by exploiting multiple antennas at the legitimate monitor, the eavesdropping non-outage probability can be significantly improved compared with the single-antenna case. In addition, the proposed suboptimal transmit zero-forcing scheme yields similar performance as the optimal scheme.
Detection , localization and characterization of damage in plates with an in situ array of spatially distributed ultrasonic sensors
(2008)

Permanently attached piezoelectric sensors arranged in a spatially distributed array are under consideration for structural health monitoring systems incorporating active ultrasonic methods. Most damage detection and localization methods that have been proposed are based upon comparing monitored signals to baselines recorded from the structure prior to initiation of damage. To be effective, this comparison process must take into account any conditions other than damage that have changed the ultrasonic signals. Proposed here is a two-step process whereby damage is first detected and is then localized and characterized. The detection strategy considers the long time behavior of the signals in the diffuse-like regime where distinct echoes can no longer be identified. The localization strategy is to generate images of damage based upon the early time regime when discrete echoes from boundary reflections and scattering sites are meaningful. Results are shown for an aluminum plate with artificial damage introduced in combination with temperature variations. The loss of local temporal coherence combined with an optimal baseline selection procedure is shown to be effective for the detection of damage, and a delay-and-sum imaging method applied to the residual signals both localizes the damage and provides characterization information. (Some figures in this article are in colour only in the electronic version)
Design, development and performance test of an automatic two-Axis solar tracker system
(2011)

The energy extracted from solar photovoltaic (PV) or solar thermal depends on solar insolation. For the extraction of maximum energy from the sun, the plane of the solar collector should always be normal to the incident radiation. The diurnal and seasonal movement of the earth affects the radiation intensity received on the solar collector. Sun trackers move the solar collector to follow the sun trajectories and keep the orientation of the solar collector at an optimal tilt angle. Energy efficiency of solar PV or solar thermal can be substantially improved using solar tracking system. In this work, an automatic solar tracking system has been designed and developed using LDR sensors and DC motors on a mechanical structure with gear arrangement. Two-axis solar tracking (azimuth angle as well as altitude angle) has been implemented through microcontroller based sophisticated control logic. Performance of the proposed system over the important parameters like solar radiation received on the collector, maximum hourly electrical power, efficiency gain, short circuit current, open circuit voltage and fill factor has been evaluated and compared with those for fixed tilt angle solar collector.
A combined pose, object, and feature model for action understanding
(2012)

Understanding natural human activity involves not only identifying the action being performed, but also locating the semantic elements of the scene and describing the person's interaction with them. We present a system that is able to recognize complex, fine-grained human actions involving the manipulation of objects in realistic action sequences. Our method takes advantage of recent advances in sensors and pose trackers in learning an action model that draws on successful discriminative techniques while explicitly modeling both pose trajectories and object manipulations. By combining these elements in a single model, we are able to simultaneously recognize actions and track the location and manipulation of objects. To showcase this ability, we introduce a novel Cooking Action Dataset that contains video, depth readings, and pose tracks from a Kinect sensor. We show that our model outperforms existing state of the art techniques on this dataset as well as the VISINT dataset with only video sequences.